def load_model(opt, pretrained_path):
seed = int(time.time())
use_cuda = True
gpus = '0'
torch.manual_seed(seed)
if use_cuda:
os.environ['CUDA_VISIBLE_DEVICES'] = gpus
torch.cuda.manual_seed(seed)
# Create model
model = YOWO(opt)
model = model.cuda()
# model = nn.DataParallel(model, device_ids=None) # in multi-gpu case
model.seen = 0
checkpoint = torch.load(pretrained_path)
epoch = checkpoint['epoch']
fscore = checkpoint['fscore']
model.load_state_dict(checkpoint['state_dict'], strict=False)
return model, epoch, fscore
anchors = [float(i) for i in anchors]
num_anchors = int(loss_options['num'])
num_classes = opt.n_classes
# Test parameters
conf_thresh = 0.005
nms_thresh = 0.4
eps = 1e-5
use_cuda = True
kwargs = {'num_workers': 0, 'pin_memory': True} if use_cuda else {}
# Create model
model = YOWO(opt)
model = model.cuda()
model = nn.DataParallel(model, device_ids=None) # in multi-gpu case
print(model)
# Load resume path
if opt.resume_path:
print("===================================================================")
print('loading checkpoint {}'.format(opt.resume_path))
checkpoint = torch.load(opt.resume_path)
model.load_state_dict(checkpoint['state_dict'])
model.eval()
print("===================================================================")
def get_clip(root, imgpath, train_dur, dataset):